Robust regression and outlier detection
Robust regression and outlier detection
Computational Statistics & Data Analysis
Self-Organizing Maps
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
RDELA--a Delaunay-triangulation-based, location and covariance estimator with high breakdown point
Statistics and Computing
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Self-organizing maps (SOMs) introduced by Kohonen (Biol. Cybern. 43(1):59---69, 1982) are well-known in the field of artificial neural networks. The way SOMs are performing is very intuitive, leading to great popularity and numerous applications (related to statistics: classification, clustering). The result of the unsupervised learning process performed by SOMs is a non-linear, low-dimensional projection of the high-dimensional input data, that preserves certain features of the underlying data, e.g.驴the topology and probability distribution (Lee and Verleysen in Nonlinear Dimensionality Reduction, Springer, 2007; Kohonen in Self-organizing Maps, 3rd edn., Springer, 2001).With the U-matrix Ultsch (Information and Classification: Concepts, Methods and Applications, pp. 307---313, Springer, 1993) introduced a powerful visual representation of the SOM results. We propose an approach that utilizes the U-matrix to identify outlying data points. Then the revised subsample (i.e. the initial sample minus the outlying points) is used to give a robust estimation of location and scatter.